Automatic Gender Classification Research Articles

Optimization of Facial Images to Predict Gender Using HDSON and Bidirectional Associative Memory

Objective: To enhance the Gender prophecy by employing facial images using imaginative algorithm and to resolve real time applications. Method: Initially, we make use of shared deep octonion network and the Octonion-Valued Neural Network (OVNN) to develop a generic framework for a Hybrid Deep Sparse Octonion Network (HDSON). Sparse Coding Octonion data Algorithm (SCOA) is exploited to depict the face images up to seven color channels and improves the weight of HDSON. Furthermore, to take advantage of the maximum storage we make use of Bidirectional Associative Memories (BAM). Findings: The proposed approach resolves both the issues of depiction of the facial image and its storage, since the present study combines the characteristics of SCOA to improve HDSON weight and BAM to enhance the storage. Moreover, the present study is simple to apply and effective in real time applications. Novelty: The proposed approach can be used in paramilitary to minimize cross border terrorism; in addition, the presented scheme can enhance the probability of child detection and may help local police to a large extent. Keywords: Automatic Gender Classification; BAM; DCN; HDSON; Octonion; SCOA; OVNN

Automatic Gender and Age Classification from Offline Handwriting with Bilinear ResNet.

This work focuses on automatic gender and age prediction tasks from handwritten documents. This problem is of interest in a variety of fields, such as historical document analysis and forensic investigations. The challenge for automatic gender and age classification can be demonstrated by the relatively low performances of the existing methods. In addition, despite the success of CNN for gender classification, deep neural networks were never applied for age classification. The published works in this area mostly concentrate on English and Arabic languages. In addition to Arabic and English, this work also considers Hebrew, which was much less studied. Following the success of bilinear Convolutional Neural Network (B-CNN) for fine-grained classification, we propose a novel implementation of a B-CNN with ResNet blocks. To our knowledge, this is the first time the bilinear CNN is applied for writer demographics classification. In particular, this is the first attempt to apply a deep neural network for the age classification. We perform experiments on documents from three benchmark datasets written in three different languages and provide a thorough comparison with the results reported in the literature. B-ResNet was top-ranked in all tasks. In particular, B-ResNet outperformed other models on KHATT and QUWI datasets on gender classification.

A real-time multi view gait-based automatic gender classification system using kinect sensor

A real-time multi view gait-based automatic gender classification system using kinect sensor

Gender Classification from Eye Images by Using Pretrained Convolutional Neural Networks

Automatic gender classification from face images has been a popular topic among researchers for a decade. Feature extraction and classification methods are very important to create a successful automatic classification system. Due to the richness of face image datasets today, many successful machine learning and deep learning methods has been implemented. It is very critical to extract accurate features from the datasets to achieve promising classification scores when traditional machine learning methods are used. However, deep learning models have been designed to extract the features automatically from the raw data directly. This also automatize the feature extraction process besides classification. The hidden and unpredictable feature sets can be explored by the deep neural networks which can increase the classification performance comparing to traditional machine learning methods. Convolutional Neural Networks (CNN) as one of the effective classes of deep models have been adopted by many scientists for solving the gender classification problem. It can solve the problem of the fact that facial cues can change from origin to origin which makes the accurate feature extraction harder. There are several state-of-the art pretrained CNN structures which are very successful for image classification problems. The performance of CNNs is generally higher when the number of the input data is high. However, in this study, the success of the pretrained CNN models is investigated when the data is limited. Considering this fact, in this study, rather than using complete face images, only the one eye image regions with eyebrows are used for the gender classification. The performance results present that the best CNN models are NASNetLarge and Xception models.

The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition

Security systems must be continuously developed in order to cope with new challenges. One example of such challenges is the proliferation of sexual harassment against women in public places, such as public toilets and public transportation. Although separately designated toilets or waiting and seating areas in public transports are provided, enforcing these restrictions need constant manual surveillance. In this paper we propose an automatic gender classification system based on an individual’s facial characteristics. We evaluate the performance of QLRBP and MLLPQ as feature extractors combined with SVM or kNN as classifiers. Our experiments show that MLLPQ gives superior performance compared to QLRBP for either classifier. Furthermore, MLLPQ is less computationally demanding compared to QLRBP. The best result we achieved in our experiments was the combination of MLLPQ and kNN classifier, yielding an accuracy rate of 92.11%.

Gender classification from speech using convolutional networks augmented with synthetic spectrograms

Automatic gender classification from speech is an integral component of human-computer interfaces. Gender information is utilized in user authentication, speech recognizers, or human-centered intelligent agents. This study focuses on gender classification from speech spectrograms using AlexNet-inspired 2D convolutional neural networks (CNN) trained on real samples augmented with synthetic spectrograms. A generative adversarial network (GAN) is trained to produce synthetic male/female-like speech spectrograms. In limited training data experiments on LibriSpeech, augmenting a training set of 200 real samples by 800 synthetic samples reduces equal error rate of the classifier from 23.7% to 1.0%. To further test the ‘quality’ of the generated samples, in a subsequent experiment, the real training samples are progressively replaced (rather than augmented) with synthetic samples at various ratios from 0 (all original samples preserved) to 1 (all original samples replaced by synthetic ones). Depending on the system setup, substituting between 50% to 90% of the original samples with the synthetic ones is found to have a minimal impact on the classifier performance. Finally, viewing the input CNN layers as filters that select salient spectrogram features, the learned convolutional kernels and filter outputs are studied to understand which spectrogram areas receive a prominent attention in the classifier.

Automatic gender detection in Twitter profiles for health-related cohort studies.

ObjectiveBiomedical research involving social media data is gradually moving from population-level to targeted, cohort-level data analysis. Though crucial for biomedical studies, social media user’s demographic information (eg, gender) is often not explicitly known from profiles. Here, we present an automatic gender classification system for social media and we illustrate how gender information can be incorporated into a social media-based health-related study.Materials and MethodsWe used a large Twitter dataset composed of public, gender-labeled users (Dataset-1) for training and evaluating the gender detection pipeline. We experimented with machine learning algorithms including support vector machines (SVMs) and deep-learning models, and public packages including M3. We considered users’ information including profile and tweets for classification. We also developed a meta-classifier ensemble that strategically uses the predicted scores from the classifiers. We then applied the best-performing pipeline to Twitter users who have self-reported nonmedical use of prescription medications (Dataset-2) to assess the system’s utility.Results and DiscussionWe collected 67 181 and 176 683 users for Dataset-1 and Dataset-2, respectively. A meta-classifier involving SVM and M3 performed the best (Dataset-1 accuracy: 94.4% [95% confidence interval: 94.0–94.8%]; Dataset-2: 94.4% [95% confidence interval: 92.0–96.6%]). Including automatically classified information in the analyses of Dataset-2 revealed gender-specific trends—proportions of females closely resemble data from the National Survey of Drug Use and Health 2018 (tranquilizers: 0.50 vs 0.50; stimulants: 0.50 vs 0.45), and the overdose Emergency Room Visit due to Opioids by Nationwide Emergency Department Sample (pain relievers: 0.38 vs 0.37).ConclusionOur publicly available, automated gender detection pipeline may aid cohort-specific social media data analyses (https://bitbucket.org/sarkerlab/gender-detection-for-public).

Human Gender Classification Using Transfer Learning via Pareto Frontier CNN Networks

Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks; GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.

Gender classification from fused multi-fingerprint types

ABSTRACT There has been a growing need for demographic information in security applications. These range from token-based demographic information elicitation to automatic gender classification or age estimation from biometric traits. Gender has been classified from facial images, voice utterances, fingerprints, hand images and emerging biometrics in the literature using local statistical or structural feature descriptors extracted from these traits. We propose a deep learning based convolutional neural network architecture for classifying gender from fingerprints of each of the five finger types and evaluate performances across trained models. We demonstrate that performance can be improved by classifying gender from fingerprints of fused combinations amongst the five right-hand finger types. The default method has been to classify gender from the index finger. However, our results show that certain finger types classify a certain gender better than the other. Leveraging on these varying strengths of the finger types we employ a fusion scheme at the abstract level, of odd number of models, trained with these fingerprint types to improve performance. Male, female and overall classification accuracies of the best fusion model are 94.7%, 88.0% and 91.3%, thus, proffering 31.02%, 7.82% and 18.72% improvement, respectively.

Automatic Gender Classification through Face Segmentation

Automatic gender classification is challenging due to large variations of face images, particularly in the un-constrained scenarios. In this paper, we propose a framework which first segments a face image into face parts, and then performs automatic gender classification. We trained a Conditional Random Fields (CRFs) based segmentation model through manually labeled face images. The CRFs based model is used to segment a face image into six different classes—mouth, hair, eyes, nose, skin, and back. The probabilistic classification strategy (PCS) is used, and probability maps are created for all six classes. We use the probability maps as gender descriptors and trained a Random Decision Forest (RDF) classifier, which classifies the face images as either male or female. The performance of the proposed framework is assessed on four publicly available datasets, namely Adience, LFW, FERET, and FEI, with results outperforming state-of-the-art (SOA).

Gender Classification Based on the Non-Lexical Cues of Emergency Calls with Recurrent Neural Networks (RNN)

Automatic gender classification in speech is a challenging research field with a wide range of applications in HCI (human-computer interaction). A couple of decades of research have shown promising results, but there is still a need for improvement. Until now, gender classification has been made using differences in the spectral characteristics of males and females. We assumed that a neutral margin exists between the male and female spectral range. This margin causes misclassification of gender. To address this limitation, we studied three non-lexical speech features (fillers, overlapping, and lengthening). From the statistical analysis, we found that overlapping and lengthening are effective in gender classification. Next, we performed gender classification using overlapping, lengthening, and the baseline acoustic feature, Mel Frequency Cepstral Coefficient (MFCC). We have tried to achieve the best results by using various combinations of features at the same time or sequentially. We used two types of machine-learning methods, support vector machine (SVM) and recurrent neural networks (RNN), to classify the gender. We achieved 89.61% with RNN using a feature set including MFCC, overlapping, and lengthening at the same time. Also, we have reclassified using non-lexical features with only data belonging to the neutral margin which was empirically selected based on the result of gender classification with only MFCC. As a result, we determined that the accuracy of classification with RNN using lengthening was 1.83% better than when MFCC alone was used. We concluded that new speech features could be effective in improving gender classification through a behavioral approach, notably including emergency calls.

Gender classification from fingerprint ridge count and fingertip size using optimal score assignment

Information on the gender of a person plays a vital role in crime investigation, authentication and statistical report on the visitors. In this work, fingerprint ridge count and fingertip size are used as the parameters for automatic gender classification. As a novel method, the optimal score assignment (OSA) method is proposed to classify gender. An optimal score is calculated for male and female from the internally collected fingerprint database. Fingerprints are collected under four age groups and all the fingers are scanned. For the fingerprint image ‘I’ for which gender is to be identified, scores are assigned for ridge count and fingertip assuming that the given image is male. A similar calculation is made assuming that the given image is female. Comparing both values, gender is declared. The maximum success rate attained is 88.41% for the age group 18–25 years and a good success rate of 90.11% is achieved for the right hand ring finger. Performance evaluation is made with the earlier findings of the author and other methods.

Joint Demographic Features Extraction for Gender, Age and Race Classification based on CNN

Automatic verification and identification of face from facial image to obtain good accuracy with huge dataset of training and testing to using face attributes from images is still challengeable. Hence proposing efficient and accurate facial image identification and classification based of facial attributes is important task. The prediction from human face image is much complex. The proposed research work for automatic gender, age and race classification is based on facial features and Convolutional Neural Network (CNN). The proposed study uses the physical appearance of human face to predict age, gender and race. The proposed methodology consists of three sub systems, Gender, Ageing and Race. Therefore different feature are extracted for every sub system. These features are extracted by using Primary, Secondary features, Face Angle, Wrinkle Analysis, LBP and WLD. The accuracy of classification is based on these features. CNN used to classify by using these features. The proposed study has been evaluated and tested on large database MORPH II and UTKF. The performance of proposed system is compared with state of art techniques.

Human Gender Prediction on Facial Mobil Images using Convolutional Neural Networks

The interest in automatic gender classification has increased rapidly, especially with the growth of online social networking platforms, social media applications, and commercial applications . Most of the images shared on these platforms are taken by mobile phone with different expressions, different angles and low resolution. In recent years, convolutional neural networks have become the most powerful method for image classification. Many researchers have shown that convolutional neural networks can achieve better performance by modifying different network layers of network architecture. Moreover, the selection of the appropriate activation function of neurons, optimizer and the loss function directly affects the performance of the convolutional neural networks. In this study, we propose a gender classification system from facial images taken by mobile phone using convolutional neural networks. The proposed convolutional neural networks have a simple network architecture with appropriate parameters can be used when rapid training is needed with the amount of limited training data. In the experimental study, the Adience benchmark dataset was used with 17492 different images with different gender and ages. The classification process was carried out by 10-fold cross validation. According the experimental results, the proposed convolutional neural networks predicted the gender of the images 98.8% correctly for training and 89.1% for testing.

Is gender encoded in the smile? A computational framework for the analysis of the smile driven dynamic face for gender recognition

Automatic gender classification has become a topic of great interest to the visual computing research community in recent times. This is due to the fact that computer-based automatic gender recognition has multiple applications including, but not limited to, face perception, age, ethnicity, identity analysis, video surveillance and smart human computer interaction. In this paper, we discuss a machine learning approach for efficient identification of gender purely from the dynamics of a person’s smile. Thus, we show that the complex dynamics of a smile on someone’s face bear much relation to the person’s gender. To do this, we first formulate a computational framework that captures the dynamic characteristics of a smile. Our dynamic framework measures changes in the face during a smile using a set of spatial features on the overall face, the area of the mouth, the geometric flow around prominent parts of the face and a set of intrinsic features based on the dynamic geometry of the face. This enables us to extract 210 distinct dynamic smile parameters which form as the contributing features for machine learning. For machine classification, we have utilised both the Support Vector Machine and the k-Nearest Neighbour algorithms. To verify the accuracy of our approach, we have tested our algorithms on two databases, namely the CK+ and the MUG, consisting of a total of 109 subjects. As a result, using the k-NN algorithm, along with tenfold cross validation, for example, we achieve an accurate gender classification rate of over 85%. Hence, through the methodology we present here, we establish proof of the existence of strong indicators of gender dimorphism, purely in the dynamics of a person’s smile.

A 3D morphometric perspective for facial gender analysis and classification using geodesic path curvature features

The relationship between the shape and gender of a face, with particular application to automatic gender classification, has been the subject of significant research in recent years. Determining the gender of a face, especially when dealing with unseen examples, presents a major challenge. This is especially true for certain age groups, such as teenagers, due to their rapid development at this phase of life. This study proposes a new set of facial morphological descriptors, based on 3D geodesic path curvatures, and uses them for gender analysis. Their goal is to discern key facial areas related to gender, specifically suited to the task of gender classification. These new curvature-based features are extracted along the geodesic path between two biological landmarks located in key facial areas.Classification performance based on the new features is compared with that achieved using the Euclidean and geodesic distance measures traditionally used in gender analysis and classification. Five different experiments were conducted on a large teenage face database (4745 faces from the Avon Longitudinal Study of Parents and Children) to investigate and justify the use of the proposed curvature features. Our experiments show that the combination of the new features with geodesic distances provides a classification accuracy of 89%. They also show that nose-related traits provide the most discriminative facial feature for gender classification, with the most discriminative features lying along the 3D face profile curve.

Human gender classification: a review

The gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis, since it contains a wide range of information regarding the characteristics difference between male and female. Some have proposed various approaches for automatic gender classification using the features derived from human bodies and/or behaviours. First, this paper introduces the challenge and application of gender classification research. Then, the development and framework of gender classification are described. We compare these state-of-the-art approaches, including vision-based methods, biological information-based methods, and social network information-based methods, to provide a comprehensive review of gender classification research. Next we highlight the strength and discuss the limitation of each method. Finally, this review also discusses several promising applications for future work.

Human gender classification: a review

Gender contains a wide range of information regarding to the characteristics difference between male and female. Successful gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis. Some have proposed various approaches for automatic gender classification using the features derived from human bodies and/or behaviors. First, this paper introduces the challenge and application for gender classification research. Then, the development and framework of gender classification are described. Besides, we compare these state-of-the-art approaches, including vision-based methods, biological information-based method, and social network information-based method, to provide a comprehensive review in the area of gender classification. In mean time, we highlight the strength and discuss the limitation of each method. Finally, this review also discusses several promising applications for the future work.

A Distribution Model with Pattern Structure in Formal Concept Analysis for Meteorological Data Minging

Automatic gender classification of an individual can be very useful in video-based surveillance systems and human-computer interaction systems. Currently, gait from a single viewpoint has been used to recognize the gender of a person. Considering the multiple cameras used in real environments, we investigate gender classification from human gait by using multi-view fusion, a relatively understudied problem. In this paper, we present a new approach to integrate information from multi-view gait at the feature level. First, gait energy images (GEI) are constructed from the video streams for different viewpoints. Then, the feature fusion is performed by putting GEI images and camera views together to generate a third-order tensor (x, y, view). A multi-linear principal component analysis (MPCA) is employed to reduce dimensionality of the tensor objects which integrate all views. The proposed fusion scheme is tested on CASIA database and compared with other fusion methods. The experimental results show that MPCA based feature fusion is quite effective for multi-view gait based gender classification.

Real-time, automatic shape-changing robot adjustment and gender classification

This paper introduces the results of novel theoretical and practical studies aimed at providing automatic and accurate real-time activation and adjustment of shape-changing robots in accord to the shape of the body of the user. The proposed method consists of scanning, classifying the instances according to gender and size, performing analysis on both the user’s body and the prospective garment, which is be virtually fitted, modelling, extracting measurements and assigning reference points on them, segmenting the 3D visual data imported from the shape-changing robot, and finally, superimposing, adopting and depicting the resulting garment model on the user’s body. The estimation process of the positions of the moving actuators for adjusting the shape-changing robots tries to determine which input values could result in the closest representation of the desired sizes and distances through devising the mathematical description of a map relating them to each other. In order to classify the data obtained by the 3D scanner, first maximum likelihood function is used for selecting one of the shape-changing robots, according to the presumed gender and size, to be activated, and subsequently, support vector machine is utilized so as to find out which shape template from the dictionary best matches the scanning instance being considered. As a use case, the proposed method is applied to the visual data obtained by scanning Fits.me’s shape-changing robots using 3D laser scanner. The methods currently used are manual, whereas the proposed method is automatic and the experimental results show that it is the accurate and reliable.